The present invention is in the field of prediction and control of neurological disturbances, particularly in the area of electrographic and clinical seizure onset prediction based on implantable devices with the major goal of alerting and/or avoiding seizures.
Approximately 1% of the world's population has epilepsy, one third of whom have seizures not controlled by medications. Some patients, whose seizures reliably begin in one discrete region, usually in the mesial (middle) temporal lobe, may be cured by epilepsy surgery. This requires removing large volumes of brain tissue, because of the lack of a reliable method to pinpoint the location of seizure onset and the pathways through which seizures spread. The 25% of refractory patients in whom surgery is not an option must resort to inadequate treatment with high doses of intoxicating medications and experimental therapies, because of poorly localized seizure onsets, multiple brain regions independently giving rise to seizures, or because their seizures originate from vital areas of the brain that cannot be removed. For these and all other epileptic patients, the utilization of a predicting device would be of invaluable help. It could prevent accidents and allow these patients to do some activities that otherwise would be risky.
Individuals with epilepsy suffer considerable disability from seizures and resulting injuries, impairment of productivity, job loss, social isolation associated with having seizures, disabling side effects from medications and other therapies. One of the most disabling aspects of epilepsy is that seizures appear to be unpredictable. However, in this invention a seizure prediction system is disclosed. Seizure prediction is a highly complex problem that involves detecting invisible and unknown patterns, as opposed to detecting visible and known patterns involved in seizure detection. To tackle such an ambitious goal, some research groups have begun developing advanced signal processing and artificial intelligence techniques. The first natural question to ask is in what ways the preictal (i.e., the period preceding the time that a seizure takes place) intracranial EEGs (IEEGs) are different from all other IEEGs segments not immediately leading to seizures. When visual pattern recognition is insufficient, quantitative EEG analysis may help extract relevant characteristic measures called features, which can then be used to make statistical inferences or to serve as inputs in automated pattern recognition systems.
Typically, the study of an event involves the goals of diagnosing (detecting) or prognosticating (predicting) such event for corrective or preventive purposes, respectively. Particularly, in the case of brain disturbances such as epileptic seizures, these two major goals have driven the efforts in the field. On one hand, there are several groups developing seizure detection methods to implement corrective techniques to stop seizures, and on the other, there are some groups investigating seizure prediction methods to provide preventive ways to avoid seizures. Among the groups claiming seizure prediction, three categories of prediction can be distinguished, clinical onset (CO) prediction, electrographic onset (EO) prediction studies, and EO prediction systems. All these categories in conjunction with seizure detection compose most of the active research in this field.
Related art approaches have focused on nonlinear methods such as studying the behavior of the principal Lyapunov exponent (PLE) in seizure EEGs, computing a correlation dimension or nonlinear chaotic analysis or determining one major feature extracted from the ictal characteristics of an electroencephalogram (EEG) or electrocorticogram (ECoG).